2024
Leveraging error-prone algorithm-derived phenotypes: Enhancing association studies for risk factors in EHR data
Lu Y, Tong J, Chubak J, Lumley T, Hubbard R, Xu H, Chen Y. Leveraging error-prone algorithm-derived phenotypes: Enhancing association studies for risk factors in EHR data. Journal Of Biomedical Informatics 2024, 157: 104690. PMID: 39004110, DOI: 10.1016/j.jbi.2024.104690.Peer-Reviewed Original ResearchElectronic health recordsElectronic health record dataKaiser Permanente WashingtonEHR-derived phenotypesAssociation studiesHealth recordsColon cancer recurrencePhenotyping errorsComputable phenotypeRisk factorsCancer recurrenceMultiple phenotypesReduce biasImprove estimation accuracySimulation studyBias reductionKaiserReduction of biasBiasEstimation accuracyAssociationStudyOutcomesRiskEstimation efficiency
2017
Identifying Metastases-related Information from Pathology Reports of Lung Cancer Patients.
Soysal E, Warner J, Denny J, Xu H. Identifying Metastases-related Information from Pathology Reports of Lung Cancer Patients. AMIA Joint Summits On Translational Science Proceedings 2017, 2017: 268-277. PMID: 28815141, PMCID: PMC5543353.Peer-Reviewed Original ResearchPathology reportsSpecimen siteImportant prognostic factorLung cancer patientsMetastatic patternPrognostic factorsClinical courseHistological typeCancer patientsMetastasis sitesMetastatic statusCancer recurrenceCancer metastasisMetastasisTumor metastasisPatientsStatus indicatorsReportStatusRecurrence